High-accuracy Dynamic Gesture Recognition: A Universal and Self-Adaptive Deep-Learning-assisted System Leveraging High-Performance Ionogels-Based Strain Sensors

Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces. However, achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors. Here, highly resilient and durable ionogels are developed by introducing micro-scale incompatible phases in macroscopic homogeneous polymeric network. The compatible network disperses in conductive ionic liquid to form highly resilient and stretchable skeleton, while incompatible phase forms hydrogen bonds to dissipate energy thus strengthening the ionogels. The ionogels-derived strain sensors show highly sensitivity, fast response time (<10 ms), low detection limit (similar to 50 mu m), and remarkable durability (>5000 cycles), allowing for precise monitoring of human motions. More importantly, a self-adaptive recognition program empowered by deep-learning algorithms is designed to compensate for sensors, creating a comprehensive system capable of dynamic gesture recognition. This system can comprehensively analyze both the temporal and spatial features of sensor data, enabling deeper understanding of the dynamic process underlying gestures. The system accurately classifies 10 hand gestures across five participants with impressive accuracy of 93.66%. Moreover, it maintains robust recognition performance without the need for further training even when different sensors or subjects are involved. This technological breakthrough paves the way for intuitive and seamless interaction between humans and machines, presenting significant opportunities in diverse applications, such as human-robot interaction, virtual reality control, and assistive devices for the disabled individuals.